Group Recommendation Algorithm Using Affinity Scores
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Solution Overview
Problem
Automatically organizing users into relevant groups in large social networking systems is inefficient, leading to mistakes and discouraging users from joining or creating groups due to the time-consuming process of manual selection.
Innovation Solution
A social networking system provides group recommendations by identifying connected users and candidate groups based on shared characteristics, using affinity scores and activity levels to suggest groups for users to join or create, and recommending users to add to existing groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual organization of groups by users is implemented, then users can identify relevant groups, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system automatically generates group recommendations using algorithms that analyze user profiles, connection data, and group characteristics. This self-service approach eliminates the need for manual group selection while maintaining high identification accuracy through automated matching of user interests and group topics.
Solution Approach 2:
The patent replaces the mechanical manual process of browsing and selecting groups with an automated computational system. The system uses algorithms to calculate compatibility scores between users and groups, automatically generating recommendations without requiring manual user intervention in the selection process.
2Productivity
If automated group organization is implemented, then the process becomes efficient, but mistakes and inaccuracies may occur
Solution Approach 1:
The system incorporates feedback mechanisms where user responses to recommendations (acceptance, rejection, or modification) are used to refine and adjust the recommendation algorithms. This continuous feedback loop improves the accuracy and reliability of automated group organization over time while maintaining high productivity.
Solution Approach 2:
The patent employs multiple adjustable parameters in the recommendation algorithm, including user interest weights, connection strength factors, and group activity metrics. These parameters can be dynamically adjusted to optimize both efficiency and accuracy, allowing the system to adapt to different user preferences and scenarios.
3Ease of operation
If users manually add users to groups, then group composition can be controlled, but the process is time-consuming and discourages group creation
Solution Approach 1:
The system performs preliminary actions by pre-identifying and ranking potential group members based on user profiles, interests, and connection data before the group creation process begins. This preliminary preparation significantly reduces the time required for group composition while maintaining control over group quality and relevance.
Solution Approach 2:
The automated user recommendation system allows groups to self-populate with relevant members based on algorithmic matching. This self-service approach to group composition eliminates the manual effort of searching and inviting users, making group creation much easier and more attractive to users.
4Measurement precision
If the system recommends groups based on connected users, then relevant groups can be identified, but the system complexity increases
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: user profile analysis, connection data processing, group characteristic evaluation, and recommendation generation. This modular segmentation manages system complexity by dividing the complex recommendation task into smaller, independently manageable components while maintaining high recommendation relevance.
Data Source
AI summary
Based on information associated with users, a social networking system recommends one or more groups for a target user to join or to create. Characteristics of the target user, characteristics of users connected to the target user, characteristics of candidate groups in the social networking system may be used to identify groups for recommendation. The social networking system may provide questions to the target user and recommend a group to the target user based on received answers to the questions. For example, the answers to the provided question identify one or more characteristics of the target user, which are used to select a group for recommendation. Additionally, the social networking system may recommend additional users for the target user to add or invite to a group based on characteristics of the target user, the additional users, and/or the group.